METHODS published: 29 March 2016 doi: 10.3389/fninf.2016.00012
Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation Richard J. Beare 1, 2 , Jian Chen 1, 2 , Claire E. Kelly 1*, Dimitrios Alexopoulos 3 , Christopher D. Smyser 3 , Cynthia E. Rogers 4 , Wai Y. Loh 1, 5 , Lillian G. Matthews 1, 6, 7 , Jeanie L. Y. Cheong 1, 7, 8 , Alicia J. Spittle 1, 7, 9 , Peter J. Anderson 1, 6 , Lex W. Doyle 1, 6, 7, 8 , Terrie E. Inder 10 , Marc L. Seal 1, 6 and Deanne K. Thompson 1, 5, 6 1
Murdoch Childrens Research Institute, The Royal Children’s Hospital, Melbourne, VIC, Australia, 2 Department of Medicine, Monash Medical Centre, Monash University, Melbourne, VIC, Australia, 3 Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA, 4 Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA, 5 Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia, 6 Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia, 7 Royal Women’s Hospital, Melbourne, VIC, Australia, 8 Department of Obstetrics and Gynaecology, University of Melbourne, Melbourne, VIC, Australia, 9 Department of Physiotherapy, University of Melbourne, Melbourne, VIC, Australia, 10 Department of Pediatric Newborn Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
Edited by: Tianzi Jiang, The Chinese Academy of Sciences, China Reviewed by: Zhengyi Yang, The University of Queensland, Australia Faguo Yang, Philips Healthcare, USA *Correspondence: Claire E. Kelly [email protected] Received: 22 December 2015 Accepted: 07 March 2016 Published: 29 March 2016 Citation: Beare RJ, Chen J, Kelly CE, Alexopoulos D, Smyser CD, Rogers CE, Loh WY, Matthews LG, Cheong JLY, Spittle AJ, Anderson PJ, Doyle LW, Inder TE, Seal ML and Thompson DK (2016) Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation. Front. Neuroinform. 10:12. doi: 10.3389/fninf.2016.00012
Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2 -weighted images of preterm infants (born ≤30 weeks’ gestation) acquired at 30 weeks’ corrected gestational age (n = 5), coronal T2 -weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n = 5) and axial T2 -weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2 -weighted images of preterm infants (born
Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation Richard J. Beare 1, 2 , Jian Chen 1, 2 , Claire E. Kelly 1*, Dimitrios Alexopoulos 3 , Christopher D. Smyser 3 , Cynthia E. Rogers 4 , Wai Y. Loh 1, 5 , Lillian G. Matthews 1, 6, 7 , Jeanie L. Y. Cheong 1, 7, 8 , Alicia J. Spittle 1, 7, 9 , Peter J. Anderson 1, 6 , Lex W. Doyle 1, 6, 7, 8 , Terrie E. Inder 10 , Marc L. Seal 1, 6 and Deanne K. Thompson 1, 5, 6 1
Murdoch Childrens Research Institute, The Royal Children’s Hospital, Melbourne, VIC, Australia, 2 Department of Medicine, Monash Medical Centre, Monash University, Melbourne, VIC, Australia, 3 Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA, 4 Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA, 5 Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia, 6 Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia, 7 Royal Women’s Hospital, Melbourne, VIC, Australia, 8 Department of Obstetrics and Gynaecology, University of Melbourne, Melbourne, VIC, Australia, 9 Department of Physiotherapy, University of Melbourne, Melbourne, VIC, Australia, 10 Department of Pediatric Newborn Medicine, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA, USA
Edited by: Tianzi Jiang, The Chinese Academy of Sciences, China Reviewed by: Zhengyi Yang, The University of Queensland, Australia Faguo Yang, Philips Healthcare, USA *Correspondence: Claire E. Kelly [email protected] Received: 22 December 2015 Accepted: 07 March 2016 Published: 29 March 2016 Citation: Beare RJ, Chen J, Kelly CE, Alexopoulos D, Smyser CD, Rogers CE, Loh WY, Matthews LG, Cheong JLY, Spittle AJ, Anderson PJ, Doyle LW, Inder TE, Seal ML and Thompson DK (2016) Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation. Front. Neuroinform. 10:12. doi: 10.3389/fninf.2016.00012
Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2 -weighted images of preterm infants (born ≤30 weeks’ gestation) acquired at 30 weeks’ corrected gestational age (n = 5), coronal T2 -weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n = 5) and axial T2 -weighted images of preterm infants acquired at 40 weeks’ corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2 -weighted images of preterm infants (born
